Road traffic congestion is the result of various phenomena often of random nature and not directly observable with empirical experiments. This makes it difficult to clearly understand the empirically observed traffic instabilities. The vehicles’ acceleration/deceleration patterns are known to trigger instabilities in the traffic flow under congestion. It has been empirically observed that free-flow pockets or voids may arise when there is a difference in the speeds and the spacing between the follower and the leader increases. During these moments, the trajectory is dictated mainly by the characteristics of the vehicle and the behaviour of the driver and not by the interactions with the leader. Voids have been identified as triggers for instabilities in both macro and micro level, which influence traffic externalities such as fuel consumption and emissions. In the literature, such behaviour is usually reproduced by injecting noise to the results of car-following models in order to create fluctuations in the instantaneous vehicles’ acceleration.

This paper proposes a novel car-following approach that takes as input the driver and the vehicle characteristics and explicitly reproduces the impact of the vehicle dynamics and the driver’s behaviour by adopting the Microsimulation Free-flow aCceleration (MFC) model. The congested part of the model corresponds to the Lagrangian discretization of the LWR model and guarantees a full consistency at the macroscopic scale with congested waves propagating accordingly to the first-order traffic flow theory.

By introducing naturalistic variation in the driving styles (timid and aggressive drivers) and the vehicle characteristics (specification from different vehicle models), the proposed model can reproduce realistic traffic flow oscillations, similar to those observed empirically. An advantage of the proposed model is that it does not require the injection of any noise in the instantaneous vehicle accelerations.

The proposed methodology has been tested by studying a) the traffic flow oscillations produced by the model in a one-lane road uphill simulation scenario, b) the ability of the model to reproduce car-following instabilities observed in three car-following trajectory datasets and c) the ability of the model to produce realistic fuel consumption estimates. The results prove the robustness of the proposed model and the ability to describe traffic flow oscillations as a consequence of the combination of driving style and vehicle’s technical specifications.

道路交通拥堵是各种现象的结果，这些现象通常是随机的，无法通过经验实验直接观察到。这使得难以清楚地理解根据经验观察到的交通不稳定性。已知车辆的加速/减速模式会在拥塞情况下触发交通流的不稳定性。根据经验已经观察到，当速度存在差异并且随动件和引导件之间的间距增加时，可能会出现自由流动的凹坑或空隙。在这些时刻，轨迹主要由车辆的特性和驾驶员的行为来决定，而不是由与领导者的互动来决定。空洞已被识别为宏观和微观层面不稳定的触发因素，影响交通外部性，例如燃料消耗和排放。在文献中，这种行为通常是通过将噪声注入到跟车模型的结果中来再现的，以便在瞬时车辆的加速度中产生波动。

本文提出了一种新的跟车方法，该方法将驾驶员和车辆特性作为输入，并通过采用微仿真自由流加速（MFC）模型来明确再现车辆动力学和驾驶员行为的影响。该模型的拥塞部分对应于LWR模型的拉格朗日离散化，并保证了在宏观尺度上的完全一致性，拥塞波根据一阶交通流理论进行了传播。

通过在驾驶方式（胆怯和好斗的驾驶员）和车辆特性（来自不同车辆模型的规格）中引入自然主义变化，所提出的模型可以重现现实的交通流量波动，类似于从经验上观察到的。所提出的模型的优点在于，它不需要在瞬时车辆加速度中注入任何噪声。

通过研究以下方法对提出的方法进行了测试：a）模型在单车道上坡模拟场景中产生的交通流量振荡； b）模型再现在三个跟车轨迹数据集中观察到的跟车不稳定性的能力，以及c）模型产生实际油耗估算的能力。结果证明了所提模型的鲁棒性以及描述由于驾驶方式和车辆技术规格而导致的交通流量波动的能力。